564report web adherence capgem healthprize

7/29/2019 564report Web Adherence Capgem Healthprize

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Estimated Annual Pharmaceutical Revenue LossDue to Medication Non-Adherence


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Executive Summary

Medication non-adherence is one o the most serious problems in healthcare, posing

a heavy fnancial impact on all constituencies. On the cost side, the New England

Healthcare Institute estimated that medication non-adherence is responsible or

$290 billion in otherwise avoidable medical spending in the US alone each year. 30

On the pharmaceutical revenue side, however, the impact o medication non-

adherence had yet to be accurately quantifed. The market assumption relied upon

to date, and quoted extensively, has been $30 billionglobally,8,10 which we elt was

a gross underestimateprompting this project. Our report represents the most

accurate estimate o pharmaceutical revenue loss due to medication non-adherence.

According to our analysis, the US pharmaceutical industry alone loses an estimated

$188 billion annually due to medication non-adherence. This represents 59% o the

$320 billion in total US pharmaceutical revenue in 2011 and 37% o the $508 billion

annualpotentialtotal revenue.

Extrapolated to the global pharmaceutical market, revenue loss is estimated to be

$564 billion, or 59% o the $956 billion in total global pharmaceutical revenue in 2011

and 37% o the $1,520 billion annualpotentialtotal revenue.

Interventions to improve medication adherence should be top priority or the

pharmaceutical industry and will prove benefcial to all stakeholders. Increasing

adherence rates by only 10 percentage points would translate into a $41 billion

pharmaceutical revenue opportunity in the US ($124 billion globally), accompanied by

improved health outcomes and decreased healthcare spending.

Thomas ForissierPrincipal, Capgemini Consulting

Katrina Firlik, MDChie Medical Ofcer,

HealthPrize Technologies

2

Lead Authors


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Introduction 4

Methods & Assumptions5

Estimate of Revenue Loss 11

Implications 15

About the Authors and Contributors 16

References 17

About Capgemini and Capgemini Consulting & HealthPrize Technologies 20

Contents

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Introduction

Medication non-adherence is one o the most serious problems in healthcare, posing

a heavy fnancial impact on all constituencies. For insurers, employers, and patients,

non-adherence signifcantly increases healthcare costs as a result o disease-related

complications. For pharmaceutical companies, pharmacies, and pharmacy benefts

managers, non-adherence signifcantly erodes proft due to prescriptions never flled

and medications not taken oten enough.

Although not the ocus o this report, non-adherence is also to blame or immense

personal and societal costs beyond the fnancial, in the orm o poor health outcomes,

untimely death, lost productivity, and compromised quality o lie. These downstream

eects are particularly tragic given their preventability.

On the cost side, the magnitude o this fnancial impact is staggering. In 2009, the New

England Healthcare Institute estimated that medication non-adherence is responsible or

$290 billion in otherwise avoidable medical spending in the US alone each year. 6

However, on the revenue side, prior to this report, the impact o medication non-

adherence had yet to be accurately quantifed. To date, the rough estimate quoted widely

in industry and analyst reports, and in the pharmaceutical press, is $30 billion in lost

pharmaceutical revenue per year globally, a statistic widely attributed to a 2006 report,8

but actually originating rom a 2004 report.10 However, even a cursory back o the

napkin calculation suggests that this number must be a gross under-representation o

the problem. It clearly looms larger, given what is known about typical adherence rates,

particularly or patients taking medications or chronic conditions. Our desire to set the

record straight, combined with the act that the $30 billion statistic remains widely quoted,was the impetus or this current report.

This report represents the most accurate estimate to date o annual pharmaceutical

revenue loss due to medication non-adherence. Although the estimation process

was driven by a set o assumptionsnecessary given a lack o complete data in all

therapeutic areas, both on the adherence side and on the pharmaceutical revenue side

the assumptions were careully thought out and inormed by the best and most recent

intelligence available. Future estimates may be more accurate in the presence o more

data, but or the time being, this report represents the most careul process to date.

The majority o modern, large-scale adherence studiesones specifcally based on

objective pharmacy claimshave been ocused on the United States. Given this ocus,

our revenue loss estimate was perormed specifcally or the United States market, with a

simple extrapolation then extended to the global market. Although such an extrapolation

is admittedly simplistic, it is likely to be reasonably accurate. According to a recent IMS

report, adherence in developing countries is similar to adherence in the United States and

other developing countries.23

Given the growing interest among pharmaceutical companies in exerting a greater

influence in promoting better outcomesand in selling wellness in addition to

medicationa ocus on medication adherence is a natural ft. In this setting, a more

accurate estimate o the impact o non-adherence on proft can serve as urther motivation

to allocate resources accordingly and perhaps as stimulus or a greater sense o urgency.

Important to emphasize, pharmaceutical-industry sponsorship o adherence interventionsare not simply sel-serving (as in, greater adherence = greater sales), even i potentially

construed in that light by a poorly inormed public or press. Improved medication

adherence represents a clear win-win or all constituencies in healthcare, not only or

insurers and employers, but alsoand most importantlyor patients.

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Methods & Assumptions

For this project, we ocused on

medications or chronic conditions.

The bulk o pharmaceutical proft

loss, as well as increased healthcare

spending, is due to poor adherence

to medications or common chronic

conditions such as hypertension,

diabetes, and high cholesterol. Non-

adherence to medications or acute

conditions would obviously increase

the estimate o annual pharmaceutical

proft loss, but we cannot speculate

with accuracy as to the magnitude o

that incremental loss, especially given

that adherence data are lacking in this

area. For our analysis, then, we simply

assumed that adherence to acute

medications is 100%. In addition, we

needed to make educated assumptions

regarding what percentage o each

chronic therapeutic area is sel-

administered on an outpatient basis

(in other words, taken by the patient

at home) and, thereore, subject to

non-adherence.

Among chronic conditions, we

examined the top 100 therapeutic areas

based upon 2011 US pharmaceutical

revenue data. Adherence data across

these therapeutic areas were gathered

rom published studies in the medical

literature. Although thousands o

articles on medication non-adherence

have been published over the past ew

decades, we determined that the vast

majority were either unreliable in terms

o methodology, out o date, or lessrelevant or various reasons, such as a

third-world ocus.

We ollowed a strict set o criteria or

inclusion o a study in our database.

First, the source o adherence data

had to include pharmacy claims

objective refll dataleading to one

or more o the ollowing standard

adherence measurements: medication

possession ratio (MPR) or proportion

o days covered (PDC). Pharmacy

claims are typically accessed via apharmacy itsel, an insurance carrier,

or a pharmacy benefts manager.

Given the nature o these studies

and their reliance on the analysis o

large-scale databases, the studies we

included were relatively recent, rom

2003 through 2012 (only one study was

older), with the vast majority o studies

published since 2008.

Study durations ranged rom 6 months

to 36 months, although only 2 studies

were shorter than 12 months (one was

6 months and one was 8 months). Most

studies included thousands o patients;some had tens o thousands. The

smallest study included 267 patients

with chronic myeloid leukemia.

We excluded studies that were based

on patient sel-reporting or on various

orms o pill tracking (manual pill

counts, electronic pill boxes, or other).

Such studies are oten compromised

by shorter durations, smaller sample

sizes, unreliable data, and by the act

that patients in these studies typically

know that they are being observed,which can artifcially raise adherence

rates (the Hawthorne eect).

Studies based on pharmacy claims

are more reliable in that they are

a more accurate reflection o

behavior in the real world (not as

part o a study or intervention) and

occur over longer time periods.

Furthermore, contemporary adherence

researchers have become increasingly

sophisticated in their methodology,

using techniques to control or variablesthat have previously led to inaccurate

adherence data. Examples o such

variables include patients who switch

pharmacies or insurance companies

or switch medications within the

same therapeutic class (which may

or may not be considered non-

adherence, depending upon whether

the perspective is that o a specifc

pharmaceutical brand or the industry

as a whole).

For purposes o this project, weconsidered the two standard

measurements o MPR and PDC to

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be sufciently equivalent and did not

attempt to adjust either. Adherence

researchers tend to become amiliar

with one or the other, and there is

not yet a gold standard in the feld.

Technically, MPR does have a slight

tendency to overestimate adherence

in the event that selected patients

repeatedly refll early (leading to a

double counting o days), but this

tendency is not consideredby most

at leastto be o signifcant concern.

An important variable in estimating

revenue loss is the need to include

statistics regarding primary non-

adherence, otherwise known as

nonulfllment, or frst-fll non-

adherence which means that a patient

never flls even the frst prescription and

thereore has an adherence rate (MPR

or PDC) o 0%, or a persistence o 0

months on therapy. However, given that

this orm o non-adherence is difcult

to track in the absence o electronic

prescribing (you cant track paper

prescriptions), inclusion o this variable

is typically lacking in adherence

studies.

However, one meta-analysis recently

concluded that the mean rate o

primary non-adherence across studies

that have been published on the topic

is 17.3%,16 and another, using a large

e-prescribing database, concluded

that the rate is 30.2%.15 We decided

to use the mean o both studies, or a

rate o 23.8%. In both papers, primary

non-adherence was also specifed

according to therapeutic area in a ew

instances, and in those instances, we

used those specifc data rather than

the more general mean. Also, where

necessary or very selected therapeutic

areas such as oncology, we estimated

a lower primary non-adherence rate,

assuming that frst flls would be higher

than average.

We also assumed, based on IMS data,

that new prescriptions, in general,

account or 10% o the overall market,

except or 10 conditions (such as

diabetes and high cholesterol), or

which we had more specifc data.

Given that, we applied the primary

non-adherence rate to that same

percentage o revenue, not to total

revenue. Continuing prescriptions

represent the majority o the market,

although the correlation between

percentage o new prescriptions and

percentage o revenue is not exact, as

new prescriptions are oten newer and

more expensive.

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Table 1. Adherence data from published, claims-based studies

Study Class or Medication Secondary Adherence

Cardiovascular

Yeaw et al, 2009 Statins 61.0%

Yeaw et al, 2009 Antihypertensives (ARBs) 66.0%

Roebucket al, 2011 Antihypertensives 59.0%

Choudhry et al, 2011 Statins, antihypertensives 62.9%

Cramer et al, 2008 Antihypertensives 67.0%

Cramer et al, 2008 Statins 74.0%

Shranket al, 2006 Calcium-channel blockers 56.5%

Shranket al, 2006 ACE inhibitors 64.9%

Shranket al, 2006 ARBs 60.7%

Shranket al, 2006 Statins 62.1%

Diabetes

Yeaw et al, 2009 Oral 72.0%

Roebucket al, 2007 Oral 40.0%60.0%

Cramer et al, 2008 Oral 71.0%

Adeyemi et al, 2012 Oral 44.7%

Zhu et al, 2011 Oral 40.0%50.0%

Rozeneld et al, 2008 Oral 81.0%

Barron et al, 2008 Oral 57.0%

Fabunmi et al, 2009 Insulin (glargine) 58.0%

Fabunmi et al, 2009 Insulin (exenetide) 68.0%

Oncology

Partridge et al, 2003 Tamoxien, breast cancer 87.0%

Partridge et al, 2008 Arimidex, breast cancer 82.0%88.0%

Wu et al, 2010 Gleevec, CML 79%

Darkow et al, 2007 Gleevec, CML 77%

Antiviral

Murphy et al, 2012 HIV (specialty pharmacy) 74.1%Murphy et al, 2012 HIV (traditional pharmacy) 69.2%

Central Nervous System

Borah et al, 2010 Alzheimers disease 75.0%

Davis et al, 2010 Parkinsons disease 58.0%

Liu et al, 2011 Depression (duloxetine) 38.1%

Liu et al, 2011 Depression (venlaaxine) 34.0%

Liu et al, 2011 Depression (escitalopram) 25.4%

Liu et al, 2011 Depression (generic SSRI) 25.5%

Barner et al, 2011 ADHD 47.4%

Ivanova et al, 2012 All MS drugs 81.1%

Reynolds et al, 2010 4 MS drugs 80.0%

Ettinger et al, 2008 Epilepsy 76.0%

Davis et al, 2008 Epilepsy 78.0%

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Study Class or Medication Secondary AdherenceRespiratory

Mattke et al, 2010 Leukotriene inhibitors 39.0%

Mattke et al, 2010 Inhaled corticosteroids 15.0%

Stempel et al, 2005 Fluticasone/salmeterol combo 23.2%

Stempel et al, 2005 Fluticasone and salmeterol 7.3%

Stempel et al, 2005 Fluticasone and montelukast 7.0%

Stempel et al, 2005 Fluticasone 8.0%

Stempel et al, 2005 Montelukast 21.4%

Yu et al, 2011 COPD single inhaler 55.0%

Yu et al, 2011 COPD multiple inhalers 51.0%

Toy et al, 2011 COPD QD inhaler 43.3%

Toy et al, 2011 COPD BID inhaler 37.0%

Toy et al, 2011 COPD TID inhaler 30.2%

Toy et al, 2011 COPD QID inhaler 23.0%

Rheumatology

Curkendall et al, 2008 RA: etanercept or adalimumab 52.0%

Stempel et al, 2005 RA: injectable, community 60.0%

Stempel et al, 2005 RA: injectable, specialty 81.0%

Stempel et al, 2005 RA: etanercept 57.0%Stempel et al, 2005 RA: anakinra 36.0%

Stempel et al, 2005 RA: inliximab 64.0%

Stempel et al, 2005 Gout: allopurinol 68.0%

Gastrointestinal

Kane et al, 2011 Ulcerative colitis: Lialda 37.2%

Kane et al, 2011 Ulcerative colitis: Asacol 23.5%

Kane et al, 2011 Ulcerative colitis: Pentasa 24.0%

Kane et al, 2011 Ulcerative colitis: balsalazide 24.0%

Kane et al, 2011 Ulcerative colitis: Dipentum 22.5%

Osteoporosis

Solomon et al, 2012 Osteoporosis 41.0%

Yeaw et al, 2009 Osteoporosis 60.0%

Ophthalmology

Gurwitz et al, 1993 Glaucoma 69.0%

Yeaw et al, 2009 Glaucoma 37.0%

Other

Nichol et al, 2009 BPH 56.0%

Yeaw et al, 2009 Incontinence/OAB 25.0%

Shranket al, 2010 Oral contraception 54.8%

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Prior to embarking on this project, we

surveyed a number o pharmaceutical

executives across divisions and

companies regarding their thoughts on

the ollowing: the priority level given to

medication non-adherence initiatives,

whether or not the industry bears a

responsibility to intervene, their level

o skepticism that the non-adherence

problem can be signifcantly improved,and fnally, their estimate o what the

pharmaceutical industry oreits each

year in the US.

We received a range o responses to

our survey. Regarding priority level,

60% elt that adherence was a high

priority or their company, whereas

20% responded highest priority

and another 20% respondents elt

the priority level was medium. None

elt that it was low priority. As or

whether or not the industry bears someresponsibility to intervene to improve

adherence, all responded yes. One

respondent stated, I believe the pharma

industry, healthcare proessionals and

payers all bear an equal responsibility

or promoting improved adherence to

medication.

Twenty percent remain slightly

skeptical that non-adherence can

be signifcantly improved, and 40%

were not sure, whereas 40% wereeither confdent or very confdent.

Regardless, one respondent stated,

It is the biggest issue aecting positive

treatment outcomes or most chronic

therapies.

Most interestingly, the range o

estimates o revenue loss by the

pharmaceutical industry due to non-

adherence was very broad, rom many

millions to $100 billion.

In contrast, based on our careul reviewo the modern claims-based adherence

literature, our estimate o revenue lost by

the pharmaceutical industry each year in

the US alone due to non-adherence to

medications or chronic disease is $188

billion (59% o the $320 billion in actual

total revenue, or 37% o the $508 billion

in potential total revenue). Extrapolated

to the global market, pharmaceutical

revenue loss is estimated to be $564

billion annually (59% o the $956 billion

in actual total global revenue, and 37%o the $1,520 billion in potential total

global revenue). This is more than 18

times higher than the $30 billion globally

most oten quoted to date.

These large numbers, particularly the

large percentages o actual revenue

lost, can be surprising at frst and are

even more surprising when you examine

specifc therapeutic classes, such as

respiratory agents and antidepressants,

where more than 200% o total current

revenues are lost due to non-adherence.How can that be? To understand this

seeming paradox, it is important to

consider this: The calculation o losses

due to non-adherence are based on

revenues that could have been earned,

not actually earned.

As a simple illustration, consider a

fctional medication with an adherence

rate o 50%, and consider that the

brand earns $100 million per year on

that medication. I all patients were,instead, ully adherent (i adherence was

100% rather than 50%), revenue would

double, to $200 million. However, as

actual adherence is only 50%, the brand

earns only hal o its potential. Another

way to express the same thought is this:

When adherence = 50%, the ratio o

revenue earned to revenue lost is 1:1. I

adherence is lower than 50%, the brand

actually loses out on more than it earns.

Estimate of Revenue Loss

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Table 2. Revenue loss by major pharmaceutical class

Non-adherence related revenue loss in the US biopharmaceutical market by major therapeutic class, 2011e, USD billion

Major TherapeuticClasses

2011US Revenue$ Million

Revenue Lost Due to Non-adherence

% Revenues Lost(revenue lost revenue earned)Primary Secondary Total

Respiratory Agents $ 21,000 $ 433$ 45,618$ 46,051 219%Antidepressants $ 11,000 $ 414 $ 23,228$ 23,642 215%Anti-ulcerants $ 10,100 $ 337$ 13,611 $ 13,949 138%Antidiabetics $ 19,600 $ 255 $ 11,155 $ 11,410 58%

Antipsychotics $ 18,200 $ 502$ 10,041 $ 10,543 58%ADHD $ 7,900 $ 129$ 10,310 $ 10,440 132%Lipid Regulators $ 20,100 $ 568$ 9,762$ 10,330 51%Autoimmune Diseases $ 12,000 $ 153$ 6,110$ 6,263 52%Angiotensin II $ 7,600 $ 120$ 4,194$ 4,314 57%Hormonal Contraceptives $ 5,200 $ 52$ 3,903$ 3,955 76%Platelet Aggregation

Inhibitors

$ 7,800 $ 128$ 3,732$ 3,859 49%HIV Antivirals $ 10,300 $ 103$ 3,709 $ 3,812 37%Oncologics $ 23,200 $ 84$ 2,153 $ 2,237 10%Anti-epileptics $ 5,900 $ 86 $ 1,436 $ 1,521 26%Multiple Sclerosis $ 7,100 $ 90$ 1,222 $ 1,312 18%Erythropoietins $ 5,100 $ 51 $ 623$ 674 13%Immunostimulating Agents $ 4,500 $ 45$ 550$ 595 13%Other $ 105,000 $ 1,024$ 31,745$ 32,769 31%Narcotic Analgesics $ 8,300 $ $ $ 0%Vaccines (Pure, Comb.,

Other)

$ 6,300 $ $ $ 0%

Antivirals, Excl. Anti-HIV $ 3,700 $ $ $ 0%Grand Total $ 319,900 $ 4,576 $ 183,102 $ 187,677 59%

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To urther clariy this picture, a ocus

on one specifc therapeutic area is

instructive. In diabetes, or example,

we examined 9 large-scale adherence

studies that met our inclusion criteria.

Two ocused on insulin therapy and

7 ocused on oral therapy. Mean MPR

or PDC or oral therapy, or example,

ranged rom 40% to 81%, with a

weighted average o 61.6%. Accounting

or primary non-adherence or the 5%

o the diabetes market that is dynamicin a given yeara critical actor not

accounted or in any o these studies

the revised weighted average adherence

rate was 60.7%. O note, all adherence

studies used or our estimate ocused

on type 2 diabetes, which represents

the vast majority o the market and the

disease variant with greater adherence

challenges.

In diabetes, total US pharmaceutical

revenues totaled $19.6 billion and

chronic use approximately $17.6

billion. Considering an estimated

mean adherence rate o 60.7% across

medications, the estimate o revenue

lost in this therapeutic area alone is

$11.4 billion or 58% o total revenue.

Although diabetes, as a common

primary care condition, is a requent

ocus o the media, we would also like

to emphasize that medication non-

adherence is surprisingly pervasive

across chronic conditions, cropping upin areas one might least expect it. Such

therapeutic areas include, or example,

immunosuppressant medications

to prevent organ rejection ater

transplantation, glaucoma medications

to prevent visual loss or blindness, HIV

medications to prolong lie, and adjuvant

therapy to prevent cancer recurrence.

There are 4 main actors that make our

estimate o revenue loss conservative.

One, we ocused only on chronic

conditions, which are the target omost adherence studies and adherence

interventions. Clearly, there are

adherence issues with nonchronic

medications, such as antibiotics or

vaccines, but we excluded those

categories altogether rom our revenue

loss calculations.

Two, the way we estimated revenue loss

due to primary non-adherence (initial

prescriptions never flled) was extremely

conservative. For lack o better sources,

the data on the relative size o the

dynamic market or new patients (versus

prescriptions renewed rom one year

to the next) that we used to calculate

our baseline were measured in total

prescriptions and were close to 10%

o the total market. However, because

new expensive drugs are generally

over-represented in comparison to older,

cheaper (and sometimes generic) drugs

in the dynamic part o the market, the

actual revenue loss due to primary non-

adherence is likely to be signifcantly

higher than our estimate.

Three, most secondary adherence

studies include only patients who haveflled a prescription at least twice (the

initial fll, with at least one refll). However,

given that the steepest drop-o in

persistence typically occurs during

the frst 3 months on therapy, many

adherence studies actually overestimate

adherence by excluding the signifcant

number o patients who fll only once.

In overestimating adherence, we

underestimate revenue loss.

And ourth, we did not account or

the act that in any given year, there

are patients who were prescribed a

medication the prior year and dropped

o, but who should have remained on

that medication through years two,

three, and beyond. The pharma industry

continues to lose out on revenue rom

these patients who should, based on

their physicians initial advice, be on

long-term or even lielong therapy.

Because we did not add these

patients to our statistics, we urther

overestimated adherence, thereby

underestimating revenue loss.

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Annual Pharmaceutical Revenue Loss Due to

Medication Non-adherence (billions)

US2004 Market Assumption

$ 14$ 30

$ 188

$ 564

Current Market Estimate

Global

Total Static Dynamic Delta Delta +

% Primary Adherence % Secondary Adherence ##% % of Actual Total

320

226

94

94

508

188

204 204

2722

183

4145

2%

57% 59%

Actual

Chronic

Split

Actual

Total

Actual Non-

chronic

Actual

Chronic

Primary

Non-

adherence

Secondary

Non-

adherence

Potential

Chronic w/o

Primary Non-

adherence

Potential

Chronic

Actual

Non-

chronic

Potential

Total

Revenue Loss

Due to Non-

adherence

Figure 1. Calculation of US pharmaceutical revenue loss due to non-adherence

Figure 2. Current revenue loss estimate compared with previous market assumption

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Implications

Our estimate o $188 billion in

pharmaceutical revenue lost annually in

the US alone and $564 billion globally

due to medication non-adherence is

the most accurate estimate to date

and points to a ar more signifcant

problem than previously believed or

acknowledged. Clearly, the priority level

assigned to medication non-adherence

should be at the highest level within

the pharmaceutical industry, and a

willingness to embrace more innovative

solutions is imperative, given the

lackluster results o historical eorts.

Although a number o pharmaceutical

companies have established

adherence teams, they are oten

underunded, slow moving, and prone

to recommending traditional tactics

such as reminder programs, cost

reductions, and isolated educational

campaigns, which are insufcient and

oten do not address the root o the

problem.

Interestingly, regardless o condition,

cost o therapy, or demographic,

a common shortcoming o human

psychology is the difculty in ollowing

through with taking a medication

(or with any healthy behavior) in

the present or a health-related

payo in the distant uture. This is a

psychological reality that tends to resist

simple reminders, cost reductions, and

even educational eorts.

However, even the most innovative and

eective solution will not cure the

problem. The goal is to raise adherence

rates compared with baseline, not to

perect adherence, which is impossible.

Given this reality, how much o the

$564 billion ($188 billion US) lost each

year can pharma reasonably expect to

recoup in the best-case scenario? This

remains unknown. However, i pharma

were able to reduce the adherence gap

by a tenth across the board, it would

net an additional $41 billion in revenue

to the US pharmaceutical industry each

year. And, with this lit in adherence

and revenues, a corresponding boost

in clinical outcomes and decline in

healthcare spending would be realized,

benefting patients and the healthcare

industry as a whole.

What is clear is that the rigor applied

to adherence research is bound to

improve, given the growing interest

in and scrutiny o the feld, and the

realization that some degree o

standardization would go ar toward

more accurate meta-analysis eorts.

Although our estimate was derived

rom the most rigorous methods

possible, combined with conservative

assumptions and the best publicly

available data, it remains an estimate

based on imperect data. We are

confdent that uture eorts, based on

better data, will oer the industry even

more accurate insights into the nature

o the problem, its magnitude, and the

efcacy o new interventions.

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About the Authors and Contributors

I you have any questions about this report, please contact:

Thomas Forissier is a Principal in Capgemini Consult ings Lie Sciences Sector and co-lead o the Lie Sciences Customer

Experience Practice. Thomas has conducted multiple projects in the areas o customer experience and digital strategy at

leading pharmaceutical companies and helped defne innovative value propositions or patients and HCPs alike. Thomas

has researched the area o patient adherence or the past ew years. He was the lead author or Capgemini Consultings

2011 Vision & Reality report Patient Adherence: The Next Frontier in Patient Care. In April 2012, he published an article in

PM360 titled Patient Adherence: Initiating the Journey toward Collaboration. He also contributed to the FirstWord 2012

report New Thinking on Patient Adherence. Thomas received masters degrees in management rom ESCP Europe and in

political science rom Science Po Paris.

Thomas ForissierPrincipal, Capgemini Consulting

Capgemini Consulting contact ino: Email: [email protected] Call: +1 646 339 6066

Dr. Katrina Firlik is co-ounder and Chie Medical Ofcer o HealthPrize Technologies. Prior to ounding HealthPrize,

Dr. Firlik was a neurosurgeon in private practice in Greenwich, Connecticut and on the clinical aculty at Yale University

School o Medicine. She is also the author oAnother Day in the Frontal Lobe, published by Random House.

Katrina Firlik, MDChie Medical Ofcer, HealthPrize Technologies

HealthPrize contact ino: Email: [email protected] Call: +1 203 957 3402

Contributors

Kevin Qin is a Senior Consultant in the Lie Sciences Practice o Capgemini Consulting.

He has an extensive background in the areas o clinical and commercial assessment

and decision modeling. Kevin holds an MBA degree rom the Indiana University at

Bloomington, Indiana.

Special thanks to Suday Karkera and Oana Hadzhiyski or their help with the analysis.

Kevin QinSenior Consultant in the Lie Sciences Practice o Capgemini Consulting

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17

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About Capgemini

HealthPrize, an innovative leader in the medication adherence space, oers an online and mobile Engagement Engine that drivesadherence via rewards, education, and gaming dynamics. The companys platorm leverages modern insights rom behavioraleconomics and consumer marketing to maximize its motivational impact. HealthPrize understands that medication non-adherenceis a complex psychological phenomenon, oten resistant to simple reminders, cost reductions, and education alone.

Find out more atwww.HealthPrize.com

Capgemini Consulting is the strategy and transformation consulting brand of Capgemini Group. The information contained in this document is proprietary.

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